The Impact of Climate Change and Human Activities on the Spatial and Temporal Variations of Vegetation NPP in the Hilly-Plain Region of Shandong Province, China

Abstract

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m⁻²·a⁻¹. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m⁻²·a⁻¹, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation’s dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals.

1. Introduction

Net primary productivity (NPP) of vegetation is the rate at which autotrophic producers within an ecosystem fix carbon through photosynthesis and produce organic matter [1]. It is not only a key indicator for measuring the ecosystem’s carbon sequestration capacity but also directly reflects the ecosystem’s productivity and potential to sustain life activities [2,3,4]. Previous research on NPP contributes significantly to determining the carbon source and storage function of ecosystems, quantifying the Earth’s carrying capacity, and analyzing the sustainable development of terrestrial ecosystems [5,6,7]. A multitude of studies have consistently indicated that climate change and human activities are the primary factors affecting the dynamics of NPP. Both factors have a significant impact on the structure, function, and quality of ecosystem services [7,8,9]. Therefore, exploring the driving characteristics of climate change and human activities on regional vegetation NPP is of great significance for maintaining ecosystem stability, sustainable development management, and ecological security.

Scientists have made significant progress in understanding the impact of climate change and human activities on the NPP in recent years [10,11,12,13]. With the continuous intensification of climate change factors and the rapid development of human society and economy, the influence of human activities becomes increasingly critical in shaping specific natural environments [11,14,15]. Among these topics, the impact of climate change on NPP has received increasing attention and is a subject of considerable focus in current global change research [1,14,16,17]. For example, climate change affects vegetation cover (FVC, NDVI) and productivity on a large scale [10,18] and has a significant impact on ecosystem structure, function, and quality of services [6,19,20,21]. Moreover, studies have demonstrated that precipitation is the main climatic driver promoting the NPP growth in the Loess Plateau and Northwest China, showing a positive impact [9,10,22,23], and its impact on vegetation growth is greater than that of temperature [10,16,23,24]. Increasing temperatures extend the vegetation’s growth season time and increase the production of cells, especially in high altitude spots such as the Qinghai Tibet Plateau [16,23,24,25]. However, the negative impact of high temperatures on the ecologically fragile karst areas in southwestern China is significant [16,23]. Related studies have confirmed that high temperatures have an inhibitory effect on vegetation growth and productivity [16,23,24,26]. Vegetation and productivity in China have been deeply affected by climate change [14,27]. These studies emphasize that climate change, especially precipitation and temperature, are important climate factors affecting vegetation cover, productivity, and spatiotemporal differences [16,22,28].

Human activities are a significant factor affecting vegetation and its productivity changes [9]. Urbanization has deepened land use changes, causing human activities to put tremendous pressure on the ecosystem since the 21st century [15,16,29]. In many regions, human activity is most directly affected by the transformation of land use types, which has become the most direct factor [14,30]. Related studies have indicated that there is a tendency for growing vegetation to be the main mechanism of NPP changes varying between research areas of China, especially in areas such as the Three-North (i.e., Northeast China, North China, and Northwest China), Southwest Karst, and Loess Plateau of China [23,24,31]. Since 1999, the Chinese government has implemented various ecological restoration projects such as afforestation, reverting cropland to forests, grasslands, etc. The implementation of these projects, coupled with the CO₂ fertilization effect, has greatly promoted vegetation restoration and productivity improvement in China [20,31]. Compared to other factors, urbanization expansion and human-induced fires have a direct, sharp declining effect on vegetation coverage and production [15]. Therefore, studying the impact of human activities on vegetation changes in different regions is of great significance for their vegetation management.

The Shandong Hilly Plain (SDHP) area is situated in China’s downstream portion of the Yellow River, a major commodity grain-producing base. However, as a hilly area in Shandong Province with a high NPP value of vegetation in the North China Plain, the hilly area of Shandong Province has a large difference in vegetation NPP estimated by different models and data. Since 2001, scholars have started using models such as CASA to estimate changes and spatiotemporal variations of vegetation NPP in the region [32]. Meanwhile, the research on vegetation NPP in Shandong Province from 2000 to 2015 also showed an overall fluctuating and increasing trend [33]. Although previous studies have shown that precipitation and temperature are important factors affecting vegetation NPP in Shandong Province [12,19,33], a deeper understanding of these impacts and how human activities interact with them remains an important research question. In particular, since 2015, there have been relatively few related research reports. Therefore, this study aims to fill this knowledge gap.

Overall, the geographical differentiation features of vegetation NPP in our research area must be seen from a fresh perspective. The quantitative analysis of the multiple driving factors affecting spatiotemporal variations in vegetation NPP still needs to be investigated. Therefore, this study used methods such as the CASA model, Theil–Sen median method, coefficient of variation, Hurst index, and geographic detector to explore the changes in vegetation NPP in the study area and address the following research questions: (1) What are the spatiotemporal dynamic characteristics (the trend, coefficient of variation, and future trend) of the NPP of vegetation in the hilly and plain areas of Shandong Province from 2000 to 2020? (2) What are the effects of driving variables such as climate change and human activities on vegetation NPP in the hilly plains of Shandong? Addressing these research questions will help us better understand the spatiotemporal evolution characteristics of vegetation NPP in the SDHP region in recent years, which is crucial for achieving ecological and sustainable development in this region.

2. Materials and Methods

2.1. Research Area

The SDHP (35°30′–37°30′ N and 116°30′–120°30′ E) is an important hilly agricultural area in northern China. As one of the three major mountainous areas of China, Shandong is mainly located in the expansive area from east of the Beijing–Hangzhou Grand Canal to south of the Yellow River. Focusing on the SDHP, this study analyzes the changes in NPP in the agricultural region of Shandong Province. This region is mainly composed of areas such as Jiaodong Hills (JDH), Northwest Plain (NWP), Jiaolai Plain (JLP), Central and Southern Hills (CSH), and Northwest Plain (NWP). The altitude range of the research area is 2 to 1545 m (Figure 1). Among them, the highest point in Shandong Province is Mount Tai in the middle, 1545 m above sea level. The lowest point is in the coastal area of the Yellow River Delta in the northeast, with an altitude of 2 to 10 m.

This research area primarily focuses on the Shandong Hills, which form the fundamental geological structure and natural frame, with agricultural plains interlaced among them. The climate belongs to the warm temperate monsoon climate type, and the vegetation is mainly composed of needle and broad-leaved tree species in the northern temperate zone. The mean annual temperature (MAT) is 11 to 14 °C, and the mean annual precipitation (MAP) is generally between 550 and 950 mm. The combination has formed a wide range of agricultural regions dominated by the SDHP. The region is also a major producer of grains and cash crops in China, which is crucial to national food security.

2.2. Data Sources

This study leverages a comprehensive dataset encompassing land use classifications [34], monthly NDVI data from 1998 to 2020, monthly average temperature data from 1901 to 2021 [35], monthly precipitation data, and a high-resolution surface solar radiation dataset with integrated sunshine hours across China [36]. Utilizing the CASA model, we derived NPP estimates for the SDHP from 2000 to 2020, cross-validated against MOD17A3 product outputs. To ascertain the determinants of NPP’s spatiotemporal variability, we conducted a correlation analysis with a suite of data encompassing terrain data (elevation), climate data (atmospheric pressure deficit, seasonal variation coefficient of precipitation), and human activity data (night-time light index). The topographic data, acquired from geospatial repositories, were processed into raster format, enabling the calculation of topographic variables such as slope and aspect.

Table 1. Data and sources.

Data Types	Factors	Resolution	Period	URL
Vegetation	Net primary productivity	30 m	2000–2020	https://lpdaac.usgs.gov/ (accessed on 15 September 2023)
	Normalized difference vegetation index	30 m	2000–2020	https://data.tpdc.ac.cn (accessed on 16 September 2023)
Climate	Surface solar radiation	1 km	2000–2017	https://data.tpdc.ac.cn (accessed on 22 September 2023)
	Seasonality of precipitation (coefficient of variation)	1 km	2000–2018	https://www.worldclim.org/data/ (accessed on 1 December 2023)
	Mean annual temperature and precipitation	1 km	1982–2022	http://www.geodata.cn/ (accessed on 18 December 2023)
	Vapor pressure deficit	1 km	2000–2020	https://climate.northwestknowledge.net/ (accessed on 1 December 2023)
Terrain	Elevation	30 m	–	http://www.gscloud.cn/ (accessed on 20 October 2023)
	Slope	30 m	–	–
	Aspect	30 m	–	–
Human activity	Land use type	30 m	2000–2020	https://zenodo.org/ (accessed on 22 December 2023)
	Night-time light index	1 km	2000–2020	https://ngdc.noaa.gov/ (accessed on 12 September 2023)

and Figure 2 provide an exhaustive outline and geographical mapping of the datasets employed in this study.

2.3. Methods

This article is based on the CASA model to calculate the NPP of the SDHP from 2000 to 2020 and uses various analysis methods such as the Theil Sen trend analysis, MK test, coefficient of variation of vegetation, Hurst index, and LUCC transfer matrix to comprehensively analyze the spatiotemporal changes of NPP in the SDHP region. Finally, the interactive impact characteristics of climate change and human activities on NPP in the region were detected by using the geographic detector method (Figure 3). All statistical analyses has been implemented by R version 4.2.1 and visualized by Origin pro 2022)

2.3.1. Land Use Change Analysis

The type of land use is a fundamental element that constitutes the regional land cover structure, and its changes affect the regional land cover pattern, which ultimately affects the land cover NPP. Therefore, the NPP dynamics within the region can be closely correlated with the transitions in land use patterns to understand the spatial distribution, changes, and transfer directions of land cover in the SDHP from 2000 to 2020 and provide support for the analysis of vegetation NPP changes caused by subsequent land cover transfer.

2.3.2. CASA Model

This study employed an upgraded CASA model to predict NPP and obtained yearly average NPP data for the SDHP from 2000 to 2020. Their estimation formula is as follows:

$$\mathrm{NPP}\left(x,t\right)=APAR\left(x,t\right)\times \mathsf{\epsilon }\left(x,t\right)$$

(1)

Among them, $APAR\left(x,t\right)$ represents the photosynthetic effective radiation absorbed by pixel x in month T, and $\left(x,t\right)$ represents the actual light energy utilization rate of pixel x in month T. The APAR can be calculated using the following Formula (2):

$$APAR\left(x,t\right)=SOL\left(x,t\right)\times FPAR\times 0.5$$

(2)

The formula uses $SOL\left(x,t\right)$ represents the total solar radiation of x pixels in the month (MJ/m²). The constant 0.5 denotes the fraction of effective solar energy (400–700 nm) that plants can absorb from total solar radiation. In addition, FPAR can be estimated by extracting the NDVI from MOD17A3 products. The conversion rate of light energy refers to the efficiency of vegetation in converting the absorbed photosynthetically active radiation (PAR) into organic carbon. This process is mainly affected by temperature and moisture. It is calculated according to Equation (3):

$$\epsilon \left(x,t\right)=T\epsilon 1\left(x,t\right)\times T\epsilon 2\left(x,t\right)\times W\epsilon \left(x,t\right)\times \epsilon max$$

(3)

In the formula, $T\epsilon 1\left(x,t\right)$ and $T\epsilon 2\left(x,t\right)$ represent the effect of temperature on the conversion rate of light energy, $W\epsilon \left(x,t\right)$ represents the influence of water conditions on the conversion rate of light energy, $\epsilon max$ represents the maximum utilization rate of light energy under ideal conditions, and its value varies greatly with different vegetation types.

Based on the method mentioned above, the estimated monthly NPP of the SDHP was used to synthesize annual NPP and check the correctness of the estimation findings. The validation of vegetation NPP often compares and analyzes the estimated values of the study area with the actual ground measurement data. However, due to the wide coverage of satellite images and the fact that the measured data and remote sensing data do not always overlap in time, it is often difficult to compare them with the data collected in the field. Therefore, this study compared and confirmed the accuracy of the CASA model estimation results with the research findings of MOD17A3 data products and related professionals.

2.3.3. Analysis of Trend and Coefficient of Variation

To analyze the trend changes of the NPP in the SDHP, we used the Theil Sen trend analysis cited in these two references [18,37] and expressed as Equation (4):

$$Slope=\mathrm{median}\left(\frac{x_j-x_i}{j-i}\right),\forall j>i$$

(4)

where j and i represent time series data. A number larger than zero shows an increasing trend in the time series, and values close to 0 indicate insignificant changes in the time series. The classification reference for significance test results is [18]. At the same time, we also conducted the Mann-Kendall significance testing on the trend of vegetation NPP changes. The significance level classification is shown in

Table 2. The Mann-Kendall test significance level.

p	Significance Level	p	Significance Level
p < 0.01	significant decrease	p < 0.01	significant increase
0.01 < p < 0.05	moderate decrease	0.01 < p < 0.05	moderate increase
p > 0.05	non-significant decrease	p > 0.05	non-significant increase

.

To better understand the changing characteristics of vegetation NPP, we selected pixel elements with no significant changes (p > 0.05) in the statistics to reduce expression interference.

The coefficient of variation (CV) [38] is a method for measuring the degree of dispersion of NPP at the pixel scale. This study uses CV to evaluate the stability of the SDHP NPP in time series and calculates Formula (5):

$$C{V}_{NPP}=\frac{{\sigma }_{NPP}}{NPP}$$

(5)

$C{V}_{NPP}$ is the coefficient of variation, NPP is the average value, and ${\mathsf{\sigma }}_{NPP}$ is the standard deviation. We used the Terra package in R language to calculate the CV of vegetation NPP in the study area. We referred to relevant research methods to classify the fluctuation characteristics of vegetation NPP into five categories based on the results of studying its coefficient of variation: Lower Fluctuation, Low Fluctuation, Moderate Fluctuation, High Fluctuation, Higher Fluctuation [37].

2.3.4. Hurst Index

This study analyzed the spatial changes of NPP time series data in the SDHP using the Hurst index [39]. The average sequence of time series is defined as follows:

$${\overline{NPP}}_{\left(\tau \right)}=\frac{1}{\tau }{\displaystyle \sum }_{t=1}^{\tau } NP{P}_{\left(\tau \right)} \tau =1,2,\cdots ,n$$

(6)

In the formula: NPP_(τ), = 1, 2, 3, 4…, n. For any positive integer greater than or equal to 1.

As follows:

$$\frac{R_{\left(\tau \right)}}{S_{\left(\tau \right)}}={(c\tau )}^H$$

(7)

The relevant values involved in calculating the Hurst index of the NPP include cumulative deviation (8), range order (9), and standard deviation order (10).

$$X_{\left(t,\tau \right)}={\displaystyle \sum }_{t=1}^t\left(NP{P}_{\left(t\right)}-\overline{NP{P}_{\left(\tau \right)}}\right),1⩽t⩽\tau$$

(8)

$$R\left(\tau \right)=max1⩽t⩽\tau X\left(t,\tau \right)-min1⩽t⩽\tau Xt,\tau ,\tau =1,2,\cdots ,n$$

(9)

$$S_{\left(\tau \right)}={\left[\frac{1}{\tau }{\displaystyle \sum }_{t=1}^{\tau }{\left(NP{P}_{\left(t\right)}-NP{P}_{\tau }\right)}^2\right]}^{\frac{1}{2}},\tau =1,2,\cdots ,n$$

(10)

According to previous research [18], the regions that have tended to increase in the past few years may increase in the coming years and vice versa. The closer it is to 1, the stronger its persistence. Based on the research results, we masked pixels without changing features (H = 0.5, p > 0.05) and expressed two characteristics: continuous decrease (0 < H < 0.05) and continuous increase (0.05 < H < 1).

2.3.5. Geographic Detector

(1)

Factor detection: Factor detection is an analysis of the single factor interpretation power of vegetation NPP that explores various influencing factors in the geographic detector [18,40,41]. Firstly, we conducted a spatial overlay analysis of the vegetation NPP layer and driving factor layer in the study area. Secondly, we adopted three discretization methods, namely natural, standard deviation, and equal spacing, to convert continuous data into categorical data to divide the driving factors of different spatial categories into sub-regions or categories. To detect the relative importance of each driving factor through significance testing, we use the size of q value to represent the explanatory power of driving factors on vegetation NPP. In the calculation process, we chose the optimal discretization method (equidistant) to limit uncertainty. The formula for calculating the q value of each explanatory variable is as follows:

$$q=1-\frac{{{\displaystyle \sum }}_{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}$$

(11)

In the formula, N represents the total number of samples in the entire study area, σ² represents the global variance of the response variable, L is the number of layers (regions), and N_h is the number of samples in layer h. σ_h² represents the variance of the predicted variable X in response to the variable in layer h. The value range of the q statistic is from 0 to 1, where the larger the q value of a single detection factor, the greater its impact on vegetation NPP changes and the greater the spatial heterogeneity of the explanatory variable (Y) along the predictive variable layer (X).

(2)

Interaction detection: Interactive detection is suitable for identifying the heterogeneity of vegetation NPP spatial changes caused by the combination of detection driving factors $X_a$ and $X_b$. The five outcomes of the interaction are described by Wang et al. [41]. This study uses the trend of the NPP change in the SDHP from 2000 to 2020 as the independent variable Y and 9 driving factors, such as terrain, climate, and human activities, as detection factor X.

(3)

Ecological detector: This model aims to determine whether there is a significant difference in the impact of any two factors on the evolution of vegetation NPP and determine whether the influence of X1 on the spatial distribution of NPP is more important than X2, measured through the F-statistic.

(4)

Risk detector: According to the risk detector, it is possible to quantitatively analyze the regions with differences in vegetation NPP change characteristics, that is, the differences in the impact of a driving factor on vegetation NPP at two different levels. The t-statistics are used to test the significance of risk detection. Therefore, we can determine the appropriate range or type of driving factors that are conducive to the growth of vegetation NPP. The range of driving factors with the highest average NPP value is suitable for vegetation NPP growth.

3. Results

3.1. Analysis of LUCC in the SDHP

Farmland encompasses the widest and largest area of land use type in the hilly plain region of Shandong Province, as depicted in Figure 2j and Figure 4. The proportion of various land use types in the region, from large to small, is as follows: farmland, building land, woodland, water bodies, grass, and shrubs (Figure 4). From 2000 to 2020, the land use situation in the areas of the SDHP gradually changed: the area of farmland and grassland progressively decreased, with a year-on-year decrease of 10.94% and 18.59%, respectively; woodland, shrubs, water bodies, and other land use show an increasing trend, with growth rates reaching 14.43%, 1.59%, 36.84%, and 5.45%, respectively. Among them, the farmland area has continued to significantly decrease, while the farmland has significantly increased. It is worth noting that the decrease in arable land is mainly due to the conversion to farmland and woodland.

3.2. CASA Model Accuracy Verification

This study compared the annual mean NPP of the SDHP simulated by the CASA model with the MOD17A3 data products from 2000 to 2020 (Figure 5). The analysis revealed a significant linear relationship (p < 0.01) between the simulated and observed datasets. Specifically, the R² values for the fit between the mean annual NPP in the study area, the farmland, and the JDH from the MOD17A3 data were 0.931, 0.869, and 0.736, respectively. Based on this, we believe that the usability of the vegetation NPP estimated by the CASA model in this article is high and is suitable for the application of quantitative analysis of vegetation NPP and related factors in the SDHP.

3.3. The Characteristics of NPP’s Spatiotemporal Variation

3.3.1. The Temporal Variation of Vegetation NPP

This study revealed that the annual mean NPP of vegetation in the SDHP from 2000 to 2020 was 220.6 ± 6.6 C, with a growth rate of 0.537 g C·m⁻²·a⁻¹ (R² = 0.502, 0.01 < p < 0.05), showing a gradual upward trend. However, it is important to note that this overall growth encompasses significant inter-annual variability, with annual NPP values fluctuating between 203 and 230 g C·m⁻²·a⁻¹, as shown in Figure 6a. The research results indicate that although vegetation NPP showed fluctuations from 2000 to 2020, overall, it still showed an upward trend. In addition, during this period, the maximum and minimum NPP values were 203 g C·m⁻²·a⁻¹ and 230 g C·m⁻²·a⁻¹, respectively, occurring in 2012 and 2003. During the period from 2000 to 2014, the NPP values varied significantly and irregularly. Since 2014, the NPP value has shown a relatively stable and slightly fluctuating trend.

NPP trends in the SDHP’s various land use types have varied, with observable fluctuations and distinct differences among vegetation types over the past two decades (Figure 6b). Analysis shows that in the past 20 years, the growth rate of woodland NPP has been 1.029 g C·m⁻²·a⁻¹, farmland NPP growth rate has been 0.62 g C·m⁻²·a⁻¹, shrub NPP growth rate has been 0.932 g C·m⁻²·a⁻¹, grassland NPP growth rate has been 0.647 g C·m⁻²·a⁻¹, and NPP growth rates for other land types have been 0.149 g C·m⁻²·a⁻¹. Meanwhile, throughout the last 20 years, yearly NPP values have varied according to plant type, with woodlands having greater annual NPP values than other vegetation types.

3.3.2. The Spatial Distribution of Vegetation NPP

In terms of spatial distribution, the vegetation NPP of the SDHP, which lasted from 2000 to 2020, showed significant spatial heterogeneity (Figure 7). Overall, its NPP shows a gradually increasing trend from southeast to northwest and from coastal to inland, and the vegetation NPP in plain areas is significantly higher than that in mountainous areas. Among them, the spatial distribution patterns of vegetation NPP and annual average NPP in the years 2000, 2005, 2015, and 2020 are consistent (Figure 7a,b,d–f); however, the spatial distribution of vegetation NPP in 2010 (Figure 7c) was significantly different, and the vegetation NPP values in the whole region were generally low. From the spatial distribution of annual average NPP, the spatial layout of vegetation NPP in the SDHP from 2000 to 2020 mainly showed a higher level in the central and northwestern regions and a lower level in the eastern, northern, and central southern regions. The areas with high NPP values mainly include the JLP, the central hilly woodland area, and the northwest plain. On the other hand, Lower NPP values are found mostly in the JDH, CSH, and northernmost regions. In addition, compared with the land use type (Figure 2j), the high-value area of the SDHP vegetation NPP is mainly distributed in woodland and plain farmland, while the low-value area is mainly located in construction land and its surrounding areas, as well as the northern mudflat area.

3.4. The Spatiotemporal Variation Index and Spatial Pattern of NPP

3.4.1. The Trend and Significance of NPP Changes

The NPP values of the SDHP image showed a slope index range of −0.68 to 0.89 from 2000 to 2020, and the spatial distribution showed significant differences (Figure 8a). Pixel-based statistical analysis of vegetation NPP variation characteristics across the region indicates that 56.18% experienced growth and 21.64% exhibited a declining trend. The findings demonstrate that the study area’s overall NPP increased between 2000 and 2020, which is consistent with the temporal variable features of NPP.

Furthermore, the significance test results of the changing trend of NPP show (Figure 8b) that the proportion of the area corresponding to the four different spatial distribution categories to the total study area has significantly increased (35.13%), mainly distributed in the central, northwestern, and eastern regions. Moderate growth (18.35%) is widely distributed throughout the study area; the moderate decrease (13.02%) is relatively small and scattered within the study area; a significant decrease (21.31%) is found in the north and south, with scattered distribution in other areas as well.

3.4.2. Characteristics of NPP Spatial Coefficient of Variation

The CV variation range of vegetation NPP in the SDHP from 2000 to 2020 was 0 to 5.18, indicating significant spatial differences in vegetation NPP, with a spatial distribution characteristic of low in the southeast and high in the northwest (Figure 8c). From the spatial distribution characteristics of the coefficient of variation of vegetation NPP in the SDHP (Figure 8c), the fluctuation level of higher fluctuation is mainly distributed in the NWP and the northern part of the CSH, accounting for 25.1%. High fluctuation is mainly distributed in the JLP, the NWP, and the surrounding areas of the CSH, accounting for 7.91%. It is worth noting that the vegetation NPP changes in the CSH and the JDH are relatively small, with low fluctuation and lower fluctuation characteristics accounting for 54.35%. The coefficient of variation of vegetation NPP in most plain areas of the SDHP is in a relatively high range, indicating that the changes in NPP in farmland are relatively unstable.

3.4.3. Prediction of NPP Change Trend

The overall trend of NPP in the SDHP mainly exhibits positive persistence or randomness (Figure 8d). The results showed that the Hurst index range of the entire study area was 0.225–0.86, with an average value of 0.556, indicating that the NPP of the SDHP showed a sustained growth trend in the time series. Among them, the area with 0 < H < 0.5 accounts for 22.35% of the total area, indicating that the NPP in this area has weak persistence or anti-persistence characteristics in the time series, mainly scattered in the CSH, JDH, and the Yellow River Delta region. The area with 0.5 < H < 1 accounts for 39.61% of the total area. This reveals a definite positive, consistent trend in vegetation NPP over time in the research region, which is broadly dispersed.

3.5. Analysis of Driving Factors for Spatiotemporal Differentiation of NPP

The q values for the NLI and land use change are the greatest, at 0.466 and 0.684, respectively (Figure 9a). These two variables have an explanatory power of more than 45%, suggesting that they are the primary drivers of vegetation NPP fluctuations in Shandong’s hilly plain. SVCP, MAP, and MAT have q values of 0.301, 0.393, and 0.378, respectively, indicating an explanatory power of more than 30%. This emphasizes the relevance of SVCP, MAP, and MAT as critical climatic variables influencing NPP fluctuations. The q values for elevation, slope, aspect, and VPD are 0.249, 0.261, 0.096, and 0.263, respectively, indicating an explanatory impact of less than 30%. Therefore, it can be assumed that elevation, slope, aspect, and VPD do not have a significant influence on NPP changes in the hilly plain of Shandong.

Table 3. Statistical significance characteristics of detection factors.

Factors	Elevation	Slope	Aspect	SVCP	VPD	MAP	MAT	LUCC	NLI
Elevation
Slope	N
Aspect	N	N
SVCP	N	N	N
VPD	N	N	N	N
MAP	N	N	N	N	N
MAT	N	N	N	N	N	N
LUCC	Y	Y	Y	Y	Y	Y	Y
NLI	N	N	N	N	N	N	N	N

Note: All factors passed significance testing (F test: 0.05). Y represents a significant difference in the impact of two factors on NPP; N represents no significant difference.

shows the spatiotemporal evolution differences of NPP for each of the nine factors. The results show that there are significant differences in the impact of LUCC and elevation, slope, aspect, VPD, SVCP, MAP, and MAT on the NPP. However, the quantitative evaluation results of vegetation NPP on the adaptive range of influencing factors indicate that the average NPP is highest in the altitude range of 309–618 m and lowest in the south and east slope directions (

Table 4. Suitable range/type of factors to vegetation net primary productivity (NPP) (confidence level 95%).

Factors	Adaptation Range/Type	Mean NPP (g C·m−2·a−1)
Elevation	309–618 m	225.45
Slope	12.9–25.7 °	219.95
Aspect	South/East	218.47
SVCP	5.8–8.7 mm	223.26
VPD	0.25–0.28 kPa	217.97
MAP	776–915 mm	222.79
MAT	11.7–12.9 °C	221.15
LUCC	Woodland/Farmland	218.91
NLI	109–327 cd/m2	217.18

).

In the analysis of interaction effects (Figure 9b), two important interactions, namely nonlinear and bilinear enhancement effects, were observed in the impact of all factors on NPP changes. As shown in Figure 9b, there are interactions between two factors with different intensities and characteristics. The interaction between MAP and LUCC [q (MAP ∩ LUCC) = 0.993] has significant explanatory power for the geographic distribution of NPP, showing significant nonlinear enhanced driving characteristics. The interaction between slope and aspect direction [q (Slope ∩ Aspect) = 0.429] has the least explanatory power for NPP and exhibits significant nonlinear enhanced driving characteristics. Furthermore, the impact of the variables on NPP variations is not independent, and the total impact of any two factors exceeds the individual effect of a single component. For example, the interaction effect [q (Elevation ∩ NLI) = 0.973] had a greater impact than Elevation (0.229) and NLI (0.466).

4. Discussion

4.1. Analysis of Spatio-Temporal Dynamics in Vegetation NPP

This study revealed significant fluctuations in the SDHP vegetation NPP between 2000 and 2020. Notwithstanding these irregularities, an overarching slow and steady growth trend was identified, which could be estimated at an average annual growth rate of 0.537 g C·m⁻²·a⁻¹. This is consistent with the trend of vegetation NPP changes in the entire North China region [12,25]. However, the interannual variation of NPP with significant fluctuations occurred only before 2014, followed by a relatively stable growth trend (Figure 6a). Other studies have also confirmed this outcome [25,33,42]. In terms of spatial differences, the high-value areas of the SDHP vegetation NPP are mainly distributed in regions such as JDH, JLP, and CSH. The vegetation NPP in hilly areas is generally higher than that in plain areas. The Shandong Hills on the North China Plain are less influenced by human activities but heavily affected by the ocean and monsoon, with water and heat conditions during the plant growth season being superior, which is conducive to vegetation growth [14,25]. However, primarily because the region is located at the mouth of the Yellow River and Bohai Bay, which has lower terrain and is susceptible to invasion by river sand and seawater [43,44]. Consequently, the area mostly comprises saline-alkali land and coastal mudflats, with high soil salt content, creating a poor growth environment for vegetation and resulting in relatively low NPP values.

Our study elucidates that there is pronounced regional variability in the NPP of vegetation across the SDHP, with the degree of change accelerating from southeast to northwest, especially pronounced in the eastern and central-southern regions (Figure 8c). Owing to the extensive expanse of arable terrain in the plains and the considerable influences of human activities on vegetation and its NPP dynamics [15,42], areas with notable fluctuations in vegetation are predominantly located within the JLP and the NWP zones. Conversely, recent measures to fortify coastal protections and ecological conservation in the SDHP region have augmented its capacity for net primary production and mitigated the previously fluctuating state of vegetation [28]. Moreover, the mountainous terrain within the Shandong Hills is largely cloaked in endemic vegetation, including woodland, shrubs, and grasslands, which is highly sensitive to climatic alterations yet exhibits a resilient vegetative consistency.

4.2. NPP’s Response to Climate-Related Factors

Several studies have identified significant geographic disparities in climate change across the Chinese Mainland in recent decades. Notably, there has been a marked rise in temperatures alongside variations in precipitation in certain areas [14,19]. Nevertheless, the regional disparities in vegetation response to climate shifts are chiefly attributed to the differing vegetation types and hydrothermal conditions prevailing across diverse locales. Congruent with research conducted within the Yellow River Basin and the arid and semi-arid regions of northwest China [10,16,22,43], our analysis discerns that the individual explanatory powers of temperature and precipitation on NPP are q (MAP) = 0.393 and q (MAT) = 0.378, respectively (Figure 9a). Such findings underscore temperature and precipitation as the principal climatic determinants impacting the NPP changes in the SDHP vegetation. This corroborates the notion that the paramount impacts of global climate change on vegetation growth are modulated by fluctuations in temperature and precipitation [19,28]. Moreover, our research intimates that varied terrestrial environments show marked responses to changes in temperature and precipitation [7,23]. When the temperature is between 11.68 and 12.91 °C and the precipitation is between 776 and 915 mm, the vegetation NPP is in the optimal range and reaches a relatively high average value. Our research results show that the interaction between precipitation and temperature [q (MAP ∩ MAT) = 0.895] significantly enhances their influence on vegetation NPP. It has been fully confirmed that suitable hydrothermal conditions are conducive to the growth of vegetation NPP.

The result shows a general trend of increasing precipitation from southeast to northwest in the regional distribution of the positive correlation and significance of precipitation on the SDHP vegetation NPP (Figure 10b,d). This is due to southeast winds controlling the temperate monsoon zone [14,19,23,27]. Therefore, considering the spatial disparities in the response of vegetation NPP to rainfall, it is noteworthy that the JLP’s geographical position near the coast results in it being significantly influenced by monsoon weather. This location faces periods of lower temperatures coupled with relatively heavier rainfall. Consequently, the region experiences a humid microclimate, which limits daylight duration and solar radiation exposure. This altered climatic pattern impacts the photosynthetic processes essential for plant growth and nutritional accumulation. Therefore, temperature and precipitation have a strong negative correlation with the region’s vegetation NPP. However, NWP is opposite to JLP, with a significant positive correlation with precipitation as the main factor. The positive correlation is most significant in the Yellow River Delta region. Thus, NWP has more dry days during the growing season compared to coastal areas, resulting in more sensitive farmland vegetation to changes in precipitation [22,44]. Due to severe soil salinization in some areas of the Yellow River Delta, precipitation dilutes the concentration of salt in the soil to a certain extent, reducing soil salinity and promoting vegetation growth. Precipitation has the greatest impact; this is consistent with the result of the highest average vegetation NPP value generated by precipitation in the range of 776–915 mm. Therefore, precipitation became the primary climatic factor limiting plant growth in the region.

Temperature, a crucial climatic variable, wields considerable influence upon the phenological and developmental processes of vegetation. In the hierarchy of explanatory variables affecting vegetative growth, it is acknowledged as the secondary determinant after precipitation, as corroborated by multiple studies [1,4,12,19,23,28]. Climate and temperature have a strong positive link in regions like the northern riparian zone, JDH, and CSH. Changes in plant production under temperature influence showed significant and extremely significant growth in areas with stable vegetation and enough precipitation (Figure 10c). Studies have shown that the conditions for vegetation growth in high latitude (high elevation) and inland semi-arid places improve with an increase in temperature and precipitation [9,16,23]. Influenced by a temperate monsoon climate, the vegetation in the northern segment of the study area, which includes the Yellow River Delta and the CSH region, maintains stability. Elevated summertime temperatures bolster plant photosynthesis, thus augmenting regional vegetation productivity [25,43,44]. Additionally, a negative correlation is discernable between temperature and the expansive developmental and farmland within this study’s purview. This is attributable to the region’s delicate vegetation, which is unable to withstand extreme temperatures [15,33].

4.3. Impact of Human Activities on NPP Changes

Climate change is the internal source of vegetation change, while human activities are the external reason [12,23,24,25,26,27,28]. Our study elucidates the correlation between the annual average NPP size of varying land use typologies within the SDHP: woodland dominates, followed by farmland, grasslands, shrubberies, and other land categories (Figure 6b). This hierarchy is deeply intertwined with the spatial distribution and transitions of vegetation NPP, as well as with forest conservation strategies and land use governance [28,43,45]. Since the 1990s, the Chinese government has proposed and implemented a series of forest protection policies, such as the “Nature Conservation Project” and the “Green Project for Returning Farmland to Forests”. These policies have directly led to a rapid increase in forest area and have continuously improved China’s vegetation productivity and carbon sequestration capacity [46]. This is consistent with the recent “Returning Farmland to Forests and Grasslands” plan in Shandong Province [47], which has improved the carbon sequestration capacity and productivity of vegetation in the region. However, it is worth noting that the NPP of vegetation in the study area has not increased continuously over the past 20 years. Instead, it has shown a fluctuating upward trend, with particularly large fluctuations before 2014 (Figure 6a). The changes in land use are an intuitive understanding of the intensity of human activities. They have both positive and negative impacts on changes in the vegetation NPP [28,45].

Conversion between different land use types can lead to changes in NPP due to the varying degrees of NPP differences between land use types [29,45]. In the study area, from 2000 to 2020, agricultural practices, urban expansion, and various forms of land conversion resulted in the conversion of arable land to urban land, which directly destroyed the original vegetation cover and reduced the available space for plant growth. This will directly lead to a decrease in NPP in or around the city [15,28,42]. Therefore, in this study, the area of low vegetation NPP is consistent with the spatial distribution of building land. That is, vegetation NPP is reduced in areas of large anthropogenic intervention. The areas with high values of vegetation NPP were mainly distributed in the northern and south-central parts of the study area, where the distribution of forest was stable, and the vegetation cover was high. Most of them were hilly and mountainous areas and riparian zones, where the anthropogenic interventions were low, the values of vegetation NPP were high, and the changes were relatively stable. As expressed in this study, farmland and woodland, among land use types, have the most significant impact on vegetation NPP (Table 4). The results of this study show the different responses of anthropogenic and natural ecosystems to environmental change.

4.4. Interactions between Climate Change and Human Activities on NPP

In this study, the interannual variation and spatial distribution of vegetation NPP in the SDHP have been elucidated through the application of a geographic detector. These findings are attributed to the combined influences of natural environmental factors and artificial protection measures. Despite providing valuable insights, the study still exhibits certain limitations. The results highlight anthropogenic factors as the primary drivers of vegetation NPP changes in the SDHP from 2000 to 2020 (Figure 9). Previous research indicated a shift in the impact of human activities from negative to positive post-2001 [48]. Notably, land use change and night lighting emerge as significant anthropogenic factors influencing vegetation dynamics [18,28,29]. It is worth noting that LUCC not only has the strongest single-factor explanatory power for vegetation NPP changes but also has a significant interaction effect with other factors, all exceeding 90% (Figure 9b). Especially in cultivated land and forest land, vegetation NPP shows a relatively high average within the appropriate range of other factors (Table 4). Moreover, the explanatory power of land use change and NLI on vegetation NPP variation surpasses that of climate and topography factors. This underscores a gradual transition of the primary drivers of vegetation NPP change in the SDHP from natural processes to human-induced factors, thereby dominating the trajectory of vegetation dynamics.

Regarding the geographic distribution, different land uses showed different NPPs [28]. Vegetation in hilly mountainous areas and riparian zones (Yellow River Delta) is relatively stable and less anthropogenic, with higher vegetation NPP values; it is mostly dominated by land types such as woodland, grassland, shrubs, etc., and vegetation changes are dominated by precipitation and temperature. The plains, on the other hand, are dominated by farmland and built-up areas with strong anthropogenic interventions, and the vegetation is more variable, constrained by land use types and urbanization (expansion of built-up areas). Previous studies have also shown that urbanization leads to an increase in NLI, which has a significant negative correlation with NDVI [18]. Furthermore, the increase in construction projects, such as highways and railways, also has a profound impact on the dynamic changes of vegetation along transportation lines [49]. Therefore, low or decreasing values of vegetation NPP were also observed. We believe that the influence of the climatic environment on the change of NPP has been weakened by the intervention of human activities in the plain area of this study. As a result, climatic variables (precipitation and temperature) are the primary climatic drivers of vegetation NPP in the study region, whereas ecological initiatives such as the conversion of cropland to the land use type (woodland and grass) are the primary human factors affecting NPP change.

4.5. Limitations and Prospects

Although the methods and framework design adopted in this study basically meet the research objectives, there are still some limitations. For example, although the validation results of the CASA model are consistent with the MOD17A3 data product, the accuracy of the model may still be limited by the quality and resolution of the data used. Furthermore, due to time scale limitations, this study was unable to reflect the trends and patterns of vegetation NPP changes over longer time scales.

Therefore, future research is expected to overcome the shortcomings of spatiotemporal change analysis in current analysis by applying multi-source, multi-temporal, and high-resolution data. Meanwhile, evaluating the long-term impact of potential climate change on regional NPP distribution in future climate model predictions will also be an important research direction. These limitations and future research prospects provide direction for future work and help the scientific community understand the applicability of current research findings and the necessity of further research.

5. Conclusions

The SDHP area, a delicate ecological region in northern China, was analyzed using the CASA model to generate data on vegetation NPP from 2000 to 2020, investigating spatial-temporal differentiation and its determinants. The conclusions are as follows.

(1)

Within the hilly and plain regions of Shandong Province, farmland represents the most expansive land use type. From 2000 to 2020, a gradual shift in land use dynamics occurred, marked by a consistent decline in the area dedicated to farmland and grasslands. In contrast, there was a concurrent increase in the area allocated to other land use types, including forests, shrubs, and water bodies. Over the course of twenty years, the average net primary productivity (NPP) of vegetation in these areas was calculated to be 220.6 ± 6.6 g C·m⁻²·a⁻¹, demonstrating a modest yet progressive increase, with an annual growth rate of 0.537 g C·m⁻²·a⁻¹. Nonetheless, it is essential to highlight that this upward trend exhibits significant variability, underlining the presence of substantial fluctuations within the overall pattern of vegetation NPP growth.

(2)

The spatiotemporal differentiation features of NPP in Shandong Province’s hilly mountainous plain exhibit high variability in terms of space. The entire vegetation NPP value is steadily growing from the coast to the interior. The degree of spatial distribution variation is low in the southeast and high in the northwest. This can be primarily attributed to the high instability of NPP in plain agriculture, while the NPP of vegetation in hilly and mountainous areas remains relatively stable. However, the future NPP in the central and southern mountainous areas, as well as the JDH areas, will have weak anti-continuity alterations, with an emphasis on development land and its surroundings.

(3)

With an explanatory power of more than 45%, the land use change and night light index have the most effects on the variation of vegetation NPP in Shandong’s plains and hilly regions. Furthermore, q values for annual average temperature, yearly average precipitation, and seasonal precipitation coefficient of variation are 0.301, 0.393, and 0.378, respectively, suggesting that these climatic factors also have a significant role in NPP changes. Thus, in Shandong’s plain and hilly regions, vegetation NPP variations are mostly caused by human activity.

The above research results have laid a solid foundation for understanding the spatiotemporal changes and determining factors of vegetation NPP in the hilly areas of the Shandong Plain. It will provide valuable references for the development of sustainable protection and management plans for vulnerable areas such as wetlands, fragile mountainous areas, and urban–rural intersections.